### Sobre o trabalho
Esse dataset com casos do COVID19, atualizados, foi criado pelo Rafael Fontes e disponibilizado no Kaggle clique aqui para acessar. Este foi um trabalho que fiz em meu curso de AI da FIAP para finalizar a disciplina de R .
## date region state cases deaths
## 1 2020-02-25 Centro-Oeste DF 0 0
## 2 2020-02-25 Centro-Oeste GO 0 0
## 3 2020-02-25 Centro-Oeste MS 0 0
## 4 2020-02-25 Centro-Oeste MT 0 0
## 5 2020-02-25 Nordeste AL 0 0
## 6 2020-02-25 Nordeste BA 0 0
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
## Loading required package: ggplot2
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## Attaching package: 'plotly'
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## last_plot
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## The following object is masked from 'package:graphics':
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## layout
## Loading required package: sp
## rgdal: version: 1.5-16, (SVN revision 1050)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.1.2, released 2020/07/07
## Path to GDAL shared files: /usr/local/Cellar/gdal/3.1.2/share/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.1.0, August 1st, 2020, [PJ_VERSION: 710]
## Path to PROJ shared files: /Users/arnaldocavalcanti/Library/Application Support/proj:/usr/local/opt/proj/share/proj:/usr/local/Cellar/proj/7.1.0/share/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-2
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## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## rgeos version: 0.5-3, (SVN revision 634)
## GEOS runtime version: 3.8.1-CAPI-1.13.3
## Linking to sp version: 1.4-2
## Polygon checking: TRUE
## date
## 1 2020-08-27
## date
## 1 2020-02-25
## 'data.frame': 4995 obs. of 5 variables:
## $ date : chr "2020-02-25" "2020-02-25" "2020-02-25" "2020-02-25" ...
## $ region: chr "Centro-Oeste" "Centro-Oeste" "Centro-Oeste" "Centro-Oeste" ...
## $ state : chr "DF" "GO" "MS" "MT" ...
## $ cases : int 0 0 0 0 0 0 0 0 0 0 ...
## $ deaths: int 0 0 0 0 0 0 0 0 0 0 ...
## `summarise()` ungrouping output (override with `.groups` argument)
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## `summarise()` ungrouping output (override with `.groups` argument)
## name cases deaths
## 1 AC 24269 607
## 2 AL 77755 1853
## 3 AM 118083 3616
## 4 AP 42285 652
## 5 BA 247853 5178
## 6 CE 210727 8365
## OGR data source with driver: GeoJSON
## Source: "/Users/arnaldocavalcanti/Documents/FIAP/Aula de R/data/brazil_geo.json", layer: "brazil_geo"
## with 27 features
## It has 2 fields
## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x -73.98971 -28.846944
## y -33.74708 5.264878
## Is projected: FALSE
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Data attributes:
## id name
## Length:27 Length:27
## Class :character Class :character
## Mode :character Mode :character
## [1] "id" "name"
## name cases deaths
## 1 AC 24269 607
## 2 AL 77755 1853
## 3 AM 118083 3616
## 4 AP 42285 652
## 5 BA 247853 5178
## 6 CE 210727 8365
## name cases deaths id
## 1 AC 24269 607 <NA>
## 2 AL 77755 1853 <NA>
## 3 AM 118083 3616 <NA>
## 4 AP 42285 652 <NA>
## 5 BA 247853 5178 <NA>
## 6 CE 210727 8365 <NA>
## 7 DF 156863 2425 <NA>
## 8 ES 108662 3105 <NA>
## 9 GO 127361 2962 <NA>
## 10 MA 148923 3402 <NA>
## 11 MG 205942 5049 <NA>
## 12 MS 46261 800 <NA>
## 13 MT 87484 2649 <NA>
## 14 PA 195297 6102 <NA>
## 15 PB 104096 2388 <NA>
## 16 PE 122147 7480 <NA>
## 17 PI 75160 1765 <NA>
## 18 PR 124074 3153 <NA>
## 19 RJ 219198 15859 <NA>
## 20 RN 60893 2219 <NA>
## 21 RO 53805 1109 <NA>
## 22 RR 42690 586 <NA>
## 23 RS 118315 3275 <NA>
## 24 SC 139638 2170 <NA>
## 25 SE 71599 1830 <NA>
## 26 SP 784453 29415 <NA>
## 27 TO 47558 635 <NA>
## [1] name cases deaths id
## <0 rows> (or 0-length row.names)
## name cases deaths id name
## 0 AC 24269 607 AC Acre
## 1 AL 77755 1853 AL Alagoas
## 2 AM 118083 3616 AP Amapá
## 3 AP 42285 652 AM Amazonas
## 4 BA 247853 5178 BA Bahia
## 5 CE 210727 8365 CE Ceará
## 6 DF 156863 2425 DF Distrito Federal
## 7 ES 108662 3105 ES Espírito Santo
## 8 GO 127361 2962 GO Goiás
## 9 MA 148923 3402 MA Maranhão
## 10 MG 205942 5049 MT Mato Grosso
## 11 MS 46261 800 MS Mato Grosso do Sul
## 12 MT 87484 2649 MG Minas Gerais
## 13 PA 195297 6102 PA Pará
## 14 PB 104096 2388 PB Paraíba
## 15 PE 122147 7480 PR Paraná
## 16 PI 75160 1765 PE Pernambuco
## 17 PR 124074 3153 PI Piauí
## 18 RJ 219198 15859 RJ Rio de Janeiro
## 19 RN 60893 2219 RN Rio Grande do Norte
## 20 RO 53805 1109 RS Rio Grande do Sul
## 21 RR 42690 586 RO Rondônia
## 22 RS 118315 3275 RR Roraima
## 23 SC 139638 2170 SC Santa Catarina
## 24 SE 71599 1830 SP São Paulo
## 25 SP 784453 29415 SE Sergipe
## 26 TO 47558 635 TO Tocantins
## estado cases deaths id name
## 0 AC 24269 607 AC Acre
## 1 AL 77755 1853 AL Alagoas
## 2 AM 118083 3616 AP Amapá
## 3 AP 42285 652 AM Amazonas
## 4 BA 247853 5178 BA Bahia
## 5 CE 210727 8365 CE Ceará
## 6 DF 156863 2425 DF Distrito Federal
## 7 ES 108662 3105 ES Espírito Santo
## 8 GO 127361 2962 GO Goiás
## 9 MA 148923 3402 MA Maranhão
## 10 MG 205942 5049 MT Mato Grosso
## 11 MS 46261 800 MS Mato Grosso do Sul
## 12 MT 87484 2649 MG Minas Gerais
## 13 PA 195297 6102 PA Pará
## 14 PB 104096 2388 PB Paraíba
## 15 PE 122147 7480 PR Paraná
## 16 PI 75160 1765 PE Pernambuco
## 17 PR 124074 3153 PI Piauí
## 18 RJ 219198 15859 RJ Rio de Janeiro
## 19 RN 60893 2219 RN Rio Grande do Norte
## 20 RO 53805 1109 RS Rio Grande do Sul
## 21 RR 42690 586 RO Rondônia
## 22 RS 118315 3275 RR Roraima
## 23 SC 139638 2170 SC Santa Catarina
## 24 SE 71599 1830 SP São Paulo
## 25 SP 784453 29415 SE Sergipe
## 26 TO 47558 635 TO Tocantins
## id name estado cases deaths
## 1 AC Acre AC 24269 607
## 2 AL Alagoas AL 77755 1853
## 3 AM Amazonas AP 42285 652
## 4 AP Amapá AM 118083 3616
## 5 BA Bahia BA 247853 5178
## 6 CE Ceará CE 210727 8365
## 7 DF Distrito Federal DF 156863 2425
## 8 ES Espírito Santo ES 108662 3105
## 9 GO Goiás GO 127361 2962
## 10 MA Maranhão MA 148923 3402
## 11 MG Minas Gerais MT 87484 2649
## 12 MS Mato Grosso do Sul MS 46261 800
## 13 MT Mato Grosso MG 205942 5049
## 14 PA Pará PA 195297 6102
## 15 PB Paraíba PB 104096 2388
## 16 PE Pernambuco PI 75160 1765
## 17 PI Piauí PR 124074 3153
## 18 PR Paraná PE 122147 7480
## 19 RJ Rio de Janeiro RJ 219198 15859
## 20 RN Rio Grande do Norte RN 60893 2219
## 21 RO Rondônia RR 42690 586
## 22 RR Roraima RS 118315 3275
## 23 RS Rio Grande do Sul RO 53805 1109
## 24 SC Santa Catarina SC 139638 2170
## 25 SE Sergipe SP 784453 29415
## 26 SP São Paulo SE 71599 1830
## 27 TO Tocantins TO 47558 635
## [1] "name" "id.x" "id.y" "estado" "cases" "deaths"
## name id.x id.y estado cases deaths
## 1 Acre AC AC AC 24269 607
## 2 Alagoas AL AL AL 77755 1853
## 3 Amapá AP AP AM 118083 3616
## 4 Amazonas AM AM AP 42285 652
## 5 Bahia BA BA BA 247853 5178
## 6 Ceará CE CE CE 210727 8365
## 7 Distrito Federal DF DF DF 156863 2425
## 8 Espírito Santo ES ES ES 108662 3105
## 9 Goiás GO GO GO 127361 2962
## 10 Maranhão MA MA MA 148923 3402
## class : SpatialPolygonsDataFrame
## features : 27
## extent : -73.98971, -28.84694, -33.74708, 5.264878 (xmin, xmax, ymin, ymax)
## variables : 6
## # A tibble: 27 x 6
## name id.x id.y estado cases deaths
## <chr> <chr> <chr> <chr> <int> <int>
## 1 Acre AC AC AC 24269 607
## 2 Alagoas AL AL AL 77755 1853
## 3 Amapá AP AP AM 118083 3616
## 4 Amazonas AM AM AP 42285 652
## 5 Bahia BA BA BA 247853 5178
## 6 Ceará CE CE CE 210727 8365
## 7 Distrito Federal DF DF DF 156863 2425
## 8 Espírito Santo ES ES ES 108662 3105
## 9 Goiás GO GO GO 127361 2962
## 10 Maranhão MA MA MA 148923 3402
## # … with 17 more rows
## [1] 24269
## [1] "Acre"